WEFNC Nexus Analysis for Agricultural Systems

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Lalit Kumar Rolaniya

Introduction

The wefnexus package provides a comprehensive toolkit for analyzing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. This vignette demonstrates a complete workflow using a conservation agriculture dataset from western Rajasthan, India.

Load Sample Data

data(arid_pulse_nexus)
d <- arid_pulse_nexus
str(d)
#> 'data.frame':    6 obs. of  26 variables:
#>  $ treatment          : chr  "CT+Flood" "CT+Sprinkler" "ZT+Drip" "ZT+Drip+Residue" ...
#>  $ tillage            : chr  "CT" "CT" "ZT" "ZT" ...
#>  $ irrigation         : chr  "Flood" "Sprinkler" "Drip" "Drip" ...
#>  $ residue            : logi  FALSE FALSE FALSE TRUE FALSE TRUE
#>  $ grain_yield        : num  980 1120 1280 1380 1450 1580
#>  $ straw_yield        : num  1450 1680 1850 2050 2180 2350
#>  $ irrigation_applied : num  280 220 160 155 135 130
#>  $ effective_rainfall : num  185 185 185 185 185 185
#>  $ total_water        : num  465 405 345 340 320 315
#>  $ crop_et            : num  390 355 310 305 290 285
#>  $ energy_input       : num  8950 7850 6480 6950 5850 6380
#>  $ energy_output_grain: num  14406 16464 18816 20286 21315 ...
#>  $ energy_output_straw: num  18125 21000 23125 25625 27250 ...
#>  $ n_applied          : num  20 20 20 20 20 20
#>  $ p_applied          : num  40 40 40 40 40 40
#>  $ k_applied          : num  0 0 0 0 0 0
#>  $ n_uptake           : num  32.5 37.8 43.2 48.5 52.1 56.8
#>  $ p_uptake           : num  4.8 5.5 6.3 7.1 7.6 8.2
#>  $ grain_n_uptake     : num  24.5 28.6 33 37.2 40.1 43.8
#>  $ diesel_use         : num  65 55 38 40 32 35
#>  $ electricity_kwh    : num  180 120 75 70 55 50
#>  $ soc_pct            : num  0.32 0.34 0.38 0.42 0.4 0.45
#>  $ bulk_density       : num  1.58 1.56 1.52 1.48 1.5 1.46
#>  $ ghg_emission       : num  1850 1520 1180 1250 1020 1100
#>  $ cost_cultivation   : num  22500 24800 21500 23200 20800 22500
#>  $ gross_return       : num  53900 61600 70400 75900 79750 ...

1. Water Module

# Water Use Efficiency
wue <- water_use_efficiency(d$grain_yield, d$total_water)
#> WUE computed: 6 values (kg/ha/mm).

# Crop Water Productivity
cwp <- crop_water_productivity(d$grain_yield, d$crop_et)
#> Crop water productivity computed (kg/m3).

# Water Footprint (green + blue)
wf <- water_footprint(
  green_water = d$effective_rainfall,
  blue_water = d$irrigation_applied,
  yield = d$grain_yield
)
#> Water footprint computed (m3/kg).
wf
#>   green_wf blue_wf grey_wf total_wf
#> 1     1.89    2.86       0     4.75
#> 2     1.65    1.96       0     3.61
#> 3     1.45    1.25       0     2.70
#> 4     1.34    1.12       0     2.46
#> 5     1.28    0.93       0     2.21
#> 6     1.17    0.82       0     1.99

2. Energy Module

# Total energy output
e_out <- d$energy_output_grain + d$energy_output_straw

# Energy Use Efficiency
eue <- energy_use_efficiency(e_out, d$energy_input)
#> EUE computed: 6 values.

# Energy Return on Investment (EROI)
eroi_values <- eroi(e_out, d$energy_input)
#> EROI computed: 3.63, 4.77, 6.47, 6.61, 8.3, 8.24 (energy-positive, energy-positive, highly profitable, highly profitable, highly profitable, highly profitable).

# Net Energy Balance
ne <- net_energy(e_out, d$energy_input)
#> Net energy computed (MJ/ha).

# Compare treatments
data.frame(Treatment = d$treatment, EUE = eue, EROI = eroi_values,
           Net_Energy = ne)
#>         Treatment  EUE EROI Net_Energy
#> 1        CT+Flood 3.63 3.63      23581
#> 2    CT+Sprinkler 4.77 4.77      29614
#> 3         ZT+Drip 6.47 6.47      35461
#> 4 ZT+Drip+Residue 6.61 6.61      38961
#> 5         PB+SSDI 8.30 8.30      42715
#> 6 PB+SSDI+Residue 8.24 8.24      46221

3. Food Module

# Harvest Index
bio_yield <- d$grain_yield + d$straw_yield
hi <- harvest_index(d$grain_yield, bio_yield)
#> Harvest index computed.

# Protein Yield (assuming 22% protein in pulses)
py <- protein_yield(d$grain_yield, protein_content = 22)
#> Protein yield computed (kg/ha).

data.frame(Treatment = d$treatment, HI = hi, Protein_kg_ha = py)
#>         Treatment     HI Protein_kg_ha
#> 1        CT+Flood 0.4033         215.6
#> 2    CT+Sprinkler 0.4000         246.4
#> 3         ZT+Drip 0.4089         281.6
#> 4 ZT+Drip+Residue 0.4023         303.6
#> 5         PB+SSDI 0.3994         319.0
#> 6 PB+SSDI+Residue 0.4020         347.6

4. Nutrient Module

# Partial Factor Productivity of N
pfp <- partial_factor_productivity(d$grain_yield, d$n_applied)
#> PFP computed (kg/kg).

# Nutrient Harvest Index for N
nhi <- nutrient_harvest_index(d$grain_n_uptake, d$n_uptake)
#> NHI computed.

data.frame(Treatment = d$treatment, PFP_N = pfp, NHI_N = nhi)
#>         Treatment PFP_N  NHI_N
#> 1        CT+Flood  49.0 0.7538
#> 2    CT+Sprinkler  56.0 0.7566
#> 3         ZT+Drip  64.0 0.7639
#> 4 ZT+Drip+Residue  69.0 0.7670
#> 5         PB+SSDI  72.5 0.7697
#> 6 PB+SSDI+Residue  79.0 0.7711

5. Carbon Module

# Carbon Footprint (IPCC AR6 GWP: CH4=27, N2O=273)
cf <- carbon_footprint(
  diesel_use = d$diesel_use[1],
  electricity_use = d$electricity_kwh[1],
  n_fertilizer = d$n_applied[1],
  p_fertilizer = d$p_applied[1],
  yield = d$grain_yield[1]
)
#> Carbon footprint: 571.2 kg CO2-eq/ha (GWP AR6: CH4=27, N2O=273).
cf$breakdown
#>         source emission_kg_CO2eq share_pct
#> 1       Diesel             174.2      30.5
#> 2  Electricity             147.6      25.8
#> 3 N_fertilizer              99.2      17.4
#> 4 P_fertilizer              64.4      11.3
#> 5 K_fertilizer               0.0       0.0
#> 6    Pesticide               0.0       0.0
#> 7         Seed               0.0       0.0
#> 8   N2O_direct              85.8      15.0
#> 9          CH4               0.0       0.0

# Soil Carbon Stock
soc <- soil_carbon_stock(d$soc_pct, d$bulk_density, depth = 30)
#> SOC stock computed (Mg C/ha).

# Global Warming Potential (example)
gwp100 <- global_warming_potential(co2 = 500, ch4 = 10, n2o = 2)
#> GWP computed: 1316 kg CO2-eq/ha (100yr horizon, IPCC AR6).
gwp20 <- global_warming_potential(co2 = 500, ch4 = 10, n2o = 2,
                                   time_horizon = "20yr")
#> GWP computed: 1856 kg CO2-eq/ha (20yr horizon, IPCC AR6).

6. Nexus Integration

# Full Nexus Summary
ns <- nexus_summary(
  yield = d$grain_yield,
  water_consumed = d$total_water,
  energy_input = d$energy_input,
  energy_output = e_out,
  n_applied = d$n_applied,
  n_uptake = d$n_uptake,
  carbon_emission = d$ghg_emission,
  treatment_names = d$treatment
)
#> Nexus summary computed for 6 treatments.
ns[, c("treatment", "EROI", "nexus_index")]
#> WEFNC Nexus Analysis Results
#> ---------------------------------------- 
#> Treatments: 6 
#> Nexus Index range: 0 - 0.978 
#> Best performer: PB+SSDI+Residue 
#> 
#>        treatment EROI nexus_index
#>         CT+Flood 3.63      0.0000
#>     CT+Sprinkler 4.77      0.2669
#>          ZT+Drip 6.47      0.5933
#>  ZT+Drip+Residue 6.61      0.6731
#>          PB+SSDI 8.30      0.8800
#>  PB+SSDI+Residue 8.24      0.9782

Radar Plot

scores <- as.matrix(ns[, c("W_score", "E_score", "F_score",
                             "N_score", "C_score")])
nexus_radar(scores, treatment_names = d$treatment)

Sustainability Scoring

nss <- nexus_sustainability_score(
  water_score = ns$W_score,
  energy_score = ns$E_score,
  food_score = ns$F_score,
  nutrient_score = ns$N_score,
  carbon_score = ns$C_score
)
#> Sustainability scores with categories computed.
nss[, c("nexus_score", "category")]
#> WEFNC Sustainability Assessment
#> ---------------------------------------- 
#>   Score: 0.0000 -> Unsustainable
#>   Score: 0.2669 -> Unsustainable
#>   Score: 0.5933 -> Low Sustainability
#>   Score: 0.6731 -> Moderately Sustainable
#>   Score: 0.8800 -> Highly Sustainable
#>   Score: 0.9782 -> Highly Sustainable
#> 
#>  nexus_score               category
#>       0.0000          Unsustainable
#>       0.2669          Unsustainable
#>       0.5933     Low Sustainability
#>       0.6731 Moderately Sustainable
#>       0.8800     Highly Sustainable
#>       0.9782     Highly Sustainable

References

Hoekstra, A.Y. et al. (2011). The Water Footprint Assessment Manual. Earthscan, London.
Hall, C.A.S. et al. (2014). EROI of different fuels and the implications for society. Energy Policy, 64, 141-152.
Dobermann, A. (2007). Nutrient use efficiency. In Fertilizer Best Management Practices, IFA, Paris.
Forster, P. et al. (2021). IPCC AR6 WGI Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity.
Lal, R. (2004). Carbon emission from farm operations. Environment International, 30(7), 981-990.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.